CN112364135A - Object pushing method, device and equipment based on multi-source data and storage medium - Google Patents
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Abstract
The invention relates to an intelligent recommendation technology and provides an object pushing method, device, equipment and storage medium based on multi-source data. The method comprises the steps of obtaining a plurality of initial keywords by obtaining a text execution preprocessing of a specific type within a preset time period, selecting a first preset number of target keywords based on frequency and a preset weight, inputting the target keywords into a first model, inputting the target keywords into a second model when the first model outputs a first result, obtaining parameter information of a plurality of initial objects corresponding to a second result when the second model outputs a second result, and screening a second preset number of target objects from the plurality of initial objects and pushing the target objects to a preset user group based on the parameter information and a plurality of preset dimensions. The invention also relates to the technical field of block chains, and the target keywords, the parameter information and the like can be stored in the nodes of a block chain.
Description
Technical Field
The invention relates to the technical field of intelligent recommendation, in particular to an object pushing method, device, equipment and storage medium based on multi-source data.
Background
Currently, with respect to recommendation of an object, an evaluator usually evaluates and recommends the target object subjectively according to some relevant attributes of the target object. For example, when an insurance company pushes insurance policy products to a user, recommendations are made according to personal information, historical insurance policies and the like of the user after evaluation according to experience, but the recommendation result is low in accuracy.
Although automatic recommendation technical solutions appear in the market, the solutions are usually performed based on a classification algorithm, and there are technical problems of low accuracy, insufficient stability, high requirement on system performance, and the like.
Disclosure of Invention
In view of the above, the present invention provides an object pushing method, device, apparatus and storage medium based on multi-source data, and aims to solve the technical problem in the prior art that the object pushing accuracy is not high.
In order to achieve the above object, the present invention provides an object pushing method based on multi-source data, including:
acquiring a specific type of text from a preset database, preprocessing the specific type of text to obtain a plurality of initial keywords, calculating the frequency of the initial keywords appearing in the specific type of text, and screening out a first preset number of target keywords based on the frequency and a preset weight;
inputting the target keywords into a first model, judging whether an output result of the first model is a first result, if so, inputting the target keywords into a second model, judging whether the output result of the second model is a second result, and if so, acquiring parameter information of a plurality of initial objects corresponding to the second result;
and screening a second preset number of target objects from the plurality of initial objects based on the parameter information and preset dimensions of the plurality of initial objects, and pushing the target objects to a preset user group.
Preferably, the preprocessing the text of the specific type to obtain a plurality of keywords includes:
and performing word segmentation operation on the specific type of text to obtain a plurality of words, and extracting a plurality of initial keywords from the plurality of words based on a TF-IDF algorithm.
Preferably, the selecting a first preset number of target keywords based on the frequency count and the preset weight includes:
and distributing associated weights for initial keywords with preset parts of speech in the plurality of initial keywords based on a preset distribution rule, calculating scores of the initial keywords based on the frequency and the weights, sequencing the scores from large to small, and selecting a first preset number of initial keywords as the target keywords.
Preferably, before the obtaining of the specific type of text from the preset database, the method further includes:
the method comprises the steps of obtaining a text of a specific type from a preset data source in real time, extracting structural information of the text of the specific type, and storing the structural information to a preset database.
Preferably, the method further comprises:
when the output result of the first model is judged not to be the first result, sending first prompt information to a preset user side;
and when the output result of the second model is judged not to be the second result, sending a second prompt message to a preset user side.
Preferably, the second model is obtained by performing a clustering operation on a preset sample object set based on a clustering algorithm, and the specific clustering operation includes:
setting a density radius between each sample object and the number of the minimum sample objects in the density radius, iteratively calculating to obtain a core sample object, a sample object with a reachable density and an edge sample object from all the sample objects based on the density radius and the number of the minimum sample objects, obtaining the sample object with the reachable density of the core sample object, and updating the cluster corresponding to the core sample object by using the sample object with the reachable density obtained by iterative calculation.
Preferably, the pushing manner includes a first pushing manner and a second pushing manner, and the first pushing manner includes: when a request for acquiring an object sent by a user in a preset user group is received, carrying out identity authentication on the user, and pushing the target object to the user when the identity authentication passes;
the second pushing manner comprises: and acquiring related information of the preset user group, wherein the related information comprises the IP address of each user, and pushing the target object to the preset user group according to the related information.
In order to achieve the above object, the present invention further provides an object pushing apparatus based on multi-source data, including:
a preprocessing module: the system comprises a database, a database and a database server, wherein the database is used for acquiring texts of specific types from a preset database, preprocessing the texts of the specific types to obtain a plurality of initial keywords, calculating the frequency of the initial keywords appearing in the texts of the specific types, and screening out a first preset number of target keywords based on the frequency and a preset weight;
a judging module: the system comprises a first model, a second model and a third model, wherein the first model is used for inputting a target keyword into the first model, judging whether an output result of the first model is a first result, if so, inputting the target keyword into the second model, judging whether an output result of the second model is a second result, and if so, acquiring parameter information of a plurality of initial objects corresponding to the second result;
a pushing module: and the target object selection module is used for screening out a second preset number of target objects from the plurality of initial objects based on the parameter information and preset dimensions of the plurality of initial objects, and pushing the target objects to a preset user group.
In order to achieve the above object, the present invention also provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a program executable by the at least one processor to enable the at least one processor to perform any of the steps of the multi-source data based object pushing method as described above.
To achieve the above object, the present invention further provides a computer-readable storage medium storing a multi-source data-based object pushing program, which when executed by a processor implements any of the steps of the multi-source data-based object pushing method as described above.
According to the object pushing method, device, equipment and storage medium based on the multi-source data, the target keywords corresponding to the text of the specific type are obtained according to the obtained text of the specific type, the target keywords are input into the model to obtain the parameter information of the plurality of initial objects, the target objects are screened out from the plurality of initial objects based on the parameter information and the plurality of preset dimensions, the target objects are pushed to the preset user group, and the target keywords obtained by combining the text of the specific type can improve the accuracy of object pushing.
Drawings
FIG. 1 is a schematic flow chart diagram illustrating a preferred embodiment of a multi-source data-based object pushing method according to the present invention;
FIG. 2 is a block diagram of an object pushing apparatus based on multi-source data according to a preferred embodiment of the present invention;
FIG. 3 is a diagram of an electronic device according to a preferred embodiment of the present invention;
the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides an object pushing method based on multi-source data. Referring to fig. 1, a schematic method flow diagram of an embodiment of the object pushing method based on multi-source data according to the present invention is shown. The method may be performed by an electronic device, which may be implemented by software and/or hardware, which may include, but is not limited to, a smartphone, a personal computer, a laptop, a tablet, a portable wearable device, and the like. The object pushing method based on the multi-source data comprises the following steps:
step S10: the method comprises the steps of obtaining a specific type of text from a preset database, carrying out preprocessing on the specific type of text to obtain a plurality of initial keywords, calculating the frequency of the initial keywords appearing in the specific type of text, and screening out a first preset number of target keywords based on the frequency and a preset weight.
In this embodiment, the policy product delivery in the insurance field is taken as an example to describe the scheme, and it should be noted that the application scenario of the scheme is not limited thereto. The preset database may be an internal database of an insurance company or a third-party database, the text of the specific type may be a web news corresponding text of a preset type (e.g., an insurance type or a social hotspot type) within a preset time period (e.g., 3 days), a preprocessing operation is performed on the text of the specific type to obtain a plurality of initial keywords, the frequency of each keyword within the preset time period (e.g., 3 days) is calculated and ranked from high to low, and a first preset number (e.g., 10) of initial keywords are selected as target keywords, for example, the selected target keywords may be typhoon, debris flow, rainstorm, and the like. Furthermore, a text library of text source dimensions and a text library of time axis dimensions can be constructed, so that a user can conveniently and quickly obtain a required text.
In one embodiment, the pre-processing the text of the specific type to obtain a plurality of keywords includes:
and performing word segmentation operation on the specific type of text to obtain a plurality of words, and extracting a plurality of initial keywords from the plurality of words based on a TF-IDF algorithm.
The forward maximum matching algorithm or the reverse maximum matching algorithm can be used for performing word segmentation on the text to obtain a plurality of words, and the specific word segmentation mode is not limited herein. And then extracting a plurality of initial keywords in the plurality of participles according to a TF-IDF algorithm, counting the word frequency of all words appearing in the text in advance, calculating an IDF value, and then calculating a TF value for each word of the text. Wherein, TF = (number of times of word appearing in text)/(total number of words of text), and the IDF value is multiplied by the TF value to obtain the TF-ID value of the word, the TF-ID value can evaluate the importance degree of the word in the text, and the larger the TF-ID value is, the higher the priority of the word is. When the TF-IDF calculation is carried out, the TF-IDF value of a certain word is obtained by the word frequency (TF) and the Inverse Document Frequency (IDF), and if the TF-IDF value is larger, the importance of the word to the text is higher, so that the word with the TF-IDF value in the front can be used as the initial key word.
In one embodiment, the selecting a first preset number of target keywords based on the frequency count and a preset weight includes:
and distributing associated weights for initial keywords with preset parts of speech in the plurality of initial keywords based on a preset distribution rule, calculating scores of the initial keywords based on the frequency and the weights, sequencing the scores from large to small, and selecting a first preset number of initial keywords as the target keywords.
Because the TF-IDF algorithm does not consider the discrimination of the position factor of the feature word to the text, when the keywords appear at different positions of the text, the contribution to the discrimination is different. For example, the text of web page news generally consists of a title and a body, and the most important parts are the title, the body beginning and the body end. The body is composed of sentences, usually the most important part of a sentence is a subject-predicate, while subjects and objects are generally nouns and predicates are generally verbs. Therefore, higher weight can be assigned to the keywords appearing at the beginning of the title, the text and the end of the text, and when the weight of the keywords is calculated, the noun > verb > adjective and adverb > other words are assigned with the weight by considering the importance degree of the part of speech. And multiplying the frequency numbers of the keywords by corresponding weights to obtain scores of the initial keywords, sequencing the scores from large to small, and selecting the initial keywords with a first preset number (for example, 10) as target keywords.
In one embodiment, before the obtaining of the specific type of text from the preset database, the method further comprises:
the method comprises the steps of obtaining a text of a specific type from a preset data source in real time, extracting structural information of the text of the specific type, and storing the structural information to a preset database.
The method has the advantages that the webpage news are crawled from various websites in real time, the structured data such as news titles, time and texts are extracted, the specific method for extracting the structured data can be realized through an OCR recognition technology, the method for extracting the structured data is not limited, the extracted structured data is stored in a preset database, and a user can conveniently obtain the specific type of texts in the preset time period.
Step S20: inputting the target keywords into a first model, judging whether an output result of the first model is a first result, if so, inputting the target keywords into a second model, judging whether an output result of the second model is a second result, and if so, acquiring parameter information of a plurality of initial objects corresponding to the second result.
In this embodiment, the early warning in the insurance recommendation field is taken as an example to explain this embodiment, a target keyword is input into a first model, the first model may be a prediction model that statistically analyzes a large amount of data of risks caused by disasters in a certain period of history, classifies a corresponding disaster type or disaster influence, determines whether an output result of the first model is a first result, the first result may be a type that the target keyword does not hit the corresponding disaster or a prediction that the target keyword does not have the corresponding disaster influence, when the output result of the first model is the first result, the target keyword is input into a second model, and determines whether an output result of the second model is a second result, the second model may be a market condition (for example, market share, product acceleration curve, sign cost rate) of various insurance policy products of the year by using a clustering algorithm, Pre-estimated endorsement generation efficiency, etc.), the second result may refer to the policy product classification after the target keyword hits the clustering in the second model, and the policy product classification may include: if the output result of the second model is a second result, acquiring parameter information of a plurality of initial objects (policy products) corresponding to the second result, wherein the parameter information comprises: the price, the type, the applicable age interval, the size of the premium and the like of the policy product.
In one embodiment, the second model is obtained by performing a clustering operation on a preset sample object set based on a clustering algorithm, and the specific clustering operation includes:
setting a density radius between each sample object and the number of the minimum sample objects in the density radius, iteratively calculating to obtain a core sample object, a sample object with a reachable density and an edge sample object from all the sample objects based on the density radius and the number of the minimum sample objects, obtaining the sample object with the reachable density of the core sample object, and updating the cluster corresponding to the core sample object by using the sample object with the reachable density obtained by iterative calculation.
The DBSCAN algorithm is a density-based clustering algorithm that generally assumes that classes can be determined by how closely the samples are distributed. The samples of the same category are closely connected, that is, samples of the same category do not exist far around any sample of the category, the closely connected samples are classified into one category, so that a clustering category is obtained, and all groups of closely connected samples are classified into different categories, so that final results of all clustering categories are obtained.
Further, as the selection of the global parameters Eps and MinPts of the DBSCAN algorithm depends on manual intervention, after the data with uniform density distribution are arranged in an ascending order according to the k-dist curve, a point at which the curve variation amplitude begins to rise steeply is manually selected as the Eps parameter, and the MinPts parameter is determined to be a fixed constant 4, the implementation process is complicated, and the manual intervention is depended on.
Therefore, the reasonable global parameters Eps and MinPts can also be determined in an adaptive manner, and part of representative objects are selected as seed objects for class expansion during region query, instead of using the neighborhood objects of all core objects as seeds for class expansion. The process is as follows:
(1) adaptively determining global parameters Eps and MinPts;
(2) classifying all sample points, respectively marking the sample points as core samples, boundary sample points and noise sample points, and deleting marked noise sample points;
(3) connecting all core points with the distances within the Eps distance, and classifying the core points into the same cluster;
(4) selecting a seed representative object corresponding to the core point in each cluster;
(5) and traversing the data sets of the labels of various policy products, performing area query according to the selected representative object, and dividing the boundary points into clusters corresponding to the core points. If all points in the data set are processed, the algorithm ends.
Because the density measurement index is single, the data set mainly aims at the data with unobvious cluster density difference. Calculating a distance distribution matrix DIST according to the input policy data set D n nxThe formula includes:
where n is the number of objects in the policy dataset D. DIST n nxIs a real symmetric matrix of n rows and n columns, where each element represents object i and object j (i.e., object j) in data set DEach product warranty). Calculating DIST n nxThe value of each element in (a) is then arranged row by row in ascending order. By DIST n ixRepresents DIST n nxThe value of the ith column in, to DIST n ixCarrying out ascending arrangement on each column to obtain KNN distribution;
and carrying out curve fitting on the KNN distribution data obtained by ascending order arrangement by using a polynomial curve fitting formula, wherein the polynomial curve fitting formula is as follows:
wherein, a, b, c, d, e are adjacent 5 sample points, x is the distance between the sample point and the abcde sample point, and x after the quadratic derivation is solved to obtain:
taking the larger value of the x solution, rounding off the smaller value, and substituting it into the above polynomial may result in EPS = f (x) since the smaller value is a point close to the boundary. Determining the MinPts value is that the object number of the Eps-neighborhood of each point is calculated in turn according to the statistical distribution characteristic of the data point of each point field, and then the mathematical expectation of the data object is calculated, wherein the formula is as follows:
where n is the number of objects in the policy dataset D, P i The number of points in the Eps neighborhood at point i.
In one embodiment, the method further comprises:
when the output result of the first model is judged not to be the first result, sending first prompt information to a preset user side; and when the output result of the second model is judged not to be the second result, sending a second prompt message to a preset user side.
When the output result of the first model is judged not to be the first result, the target keyword hits the corresponding disaster type, or the target keyword is predicted to have the corresponding disaster influence, so that the corresponding early warning prompt can be sent to a manager, and when the output result of the second model is judged not to be the second result, the target keyword is not in the existing accumulation of insurance policy products, and the insurance policy can be planned for the insurance policy target or new positive influence is generated on customer service, so that the prompt information can be sent for a product decision maker to refer to.
Step S30: and screening a second preset number of target objects from the plurality of initial objects based on the parameter information and preset dimensions of the plurality of initial objects, and pushing the target objects to a preset user group.
In this embodiment, a preset number of target objects can be screened from a plurality of initial objects (policy products) according to the continuation policy, the cost policy, the acceleration policy of the plurality of initial objects and the parameter information of the plurality of initial objects, and the target objects are pushed to a preset user group, for example, by calculating and summarizing historical policies, 3 sets of policy products with the highest continuation rate after the customer policy is due under the conditions of different insurance application price intervals, different payment intervals, insurance application time limits, different insurance coverage, different age groups of insurance applicants, different vehicle attributions, and the like are judged;
calculating the insurance policy of the type by the cost dimension, subtracting the payment cost from the application expense under the condition ranges of different sign sheet cost rates, price intervals and the like, obtaining 3 sets of insurance policy products with the optimal difference value within a certain requirement range, calculating 3 sets of insurance policy products with the fastest insurance policy acceleration ratio under different conditions by the acceleration dimension, and pushing the calculated insurance policy products to a preset user group.
In one embodiment, the pushing manner includes a first pushing manner and a second pushing manner, and the first pushing manner includes: when a request for acquiring an object sent by a user in a preset user group is received, carrying out identity authentication on the user, and pushing the target object to the user when the identity authentication passes;
the second pushing manner comprises: and acquiring related information of the preset user group, wherein the related information comprises the IP address of each user, and pushing the target object to the preset user group according to the related information.
In a first push mode (e.g., active acquisition by a user), the user is authenticated upon receiving a request for object acquisition from the user. For example, the device identifier included in the request is obtained, whether the device identifier is a pre-bound device identifier (white list) is judged, if yes, the request is judged to be normal, and if not, the request is judged to be abnormal. And a second pushing mode (for example, passive receiving by the user) acquires the IP address of the user, and pushes the target object to the corresponding user.
Referring to fig. 2, a functional block diagram of the object pushing apparatus 100 based on multi-source data according to the present invention is shown.
The object pushing device 100 based on multi-source data can be installed in an electronic device. According to the implemented functions, the object pushing device 100 based on multi-source data may include a preprocessing module 110, a determining module 120 and a pushing module 130. A module according to the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the preprocessing module 110 is configured to obtain a specific type of text from a preset database, perform preprocessing on the specific type of text to obtain a plurality of initial keywords, calculate a frequency count of the initial keywords appearing in the specific type of text, and screen out a first preset number of target keywords based on the frequency count and a preset weight.
In this embodiment, the policy product delivery in the insurance field is taken as an example to describe the scheme, and it should be noted that the application scenario of the scheme is not limited thereto. The preset database may be an internal database of an insurance company or a third-party database, the text of the specific type may be a web news corresponding text of a preset type (e.g., an insurance type or a social hotspot type) within a preset time period (e.g., 3 days), a preprocessing operation is performed on the text of the specific type to obtain a plurality of initial keywords, the frequency of each keyword within the preset time period (e.g., 3 days) is calculated and ranked from high to low, and a first preset number (e.g., 10) of initial keywords are selected as target keywords, for example, the selected target keywords may be typhoon, debris flow, rainstorm, and the like. Furthermore, a text library of text source dimensions and a text library of time axis dimensions can be constructed, so that a user can conveniently and quickly obtain a required text.
In one embodiment, the pre-processing the text of the specific type to obtain a plurality of keywords includes:
and performing word segmentation operation on the specific type of text to obtain a plurality of words, and extracting a plurality of initial keywords from the plurality of words based on a TF-IDF algorithm.
The forward maximum matching algorithm or the reverse maximum matching algorithm can be used for performing word segmentation on the text to obtain a plurality of words, and the specific word segmentation mode is not limited herein. And then extracting a plurality of initial keywords in the plurality of participles according to a TF-IDF algorithm, counting the word frequency of all words appearing in the text in advance, calculating an IDF value, and then calculating a TF value for each word of the text. Wherein, TF = (number of times of word appearing in text)/(total number of words of text), and the IDF value is multiplied by the TF value to obtain the TF-ID value of the word, the TF-ID value can evaluate the importance degree of the word in the text, and the larger the TF-ID value is, the higher the priority of the word is. When the TF-IDF calculation is carried out, the TF-IDF value of a certain word is obtained by the word frequency (TF) and the Inverse Document Frequency (IDF), and if the TF-IDF value is larger, the importance of the word to the text is higher, so that the word with the TF-IDF value in the front can be used as the initial key word.
In one embodiment, the selecting a first preset number of target keywords based on the frequency count and a preset weight includes:
and distributing associated weights for initial keywords with preset parts of speech in the plurality of initial keywords based on a preset distribution rule, calculating scores of the initial keywords based on the frequency and the weights, sequencing the scores from large to small, and selecting a first preset number of initial keywords as the target keywords.
Because the TF-IDF algorithm does not consider the discrimination of the position factor of the feature word to the text, when the keywords appear at different positions of the text, the contribution to the discrimination is different. For example, the text of web page news generally consists of a title and a body, and the most important parts are the title, the body beginning and the body end. The body is composed of sentences, usually the most important part of a sentence is a subject-predicate, while subjects and objects are generally nouns and predicates are generally verbs. Therefore, higher weight can be assigned to the keywords appearing at the beginning of the title, the text and the end of the text, and when the weight of the keywords is calculated, the noun > verb > adjective and adverb > other words are assigned with the weight by considering the importance degree of the part of speech. And multiplying the frequency numbers of the keywords by corresponding weights to obtain scores of the initial keywords, sequencing the scores from large to small, and selecting the initial keywords with a first preset number (for example, 10) as target keywords.
In one embodiment, before the obtaining of the specific type of text from the preset database, the preprocessing module is further configured to:
the method comprises the steps of obtaining a text of a specific type from a preset data source in real time, extracting structural information of the text of the specific type, and storing the structural information to a preset database.
The method has the advantages that the webpage news are crawled from various websites in real time, the structured data such as news titles, time and texts are extracted, the specific method for extracting the structured data can be realized through an OCR recognition technology, the method for extracting the structured data is not limited, the extracted structured data is stored in a preset database, and a user can conveniently obtain the specific type of texts in the preset time period.
The determining module 120 is configured to input the target keyword into the first model, determine whether an output result of the first model is a first result, if so, input the target keyword into the second model, determine whether an output result of the second model is a second result, and if so, obtain parameter information of a plurality of initial objects corresponding to the second result.
In this embodiment, the early warning in the insurance recommendation field is taken as an example to explain this embodiment, a target keyword is input into a first model, the first model may be a prediction model that statistically analyzes a large amount of data of risks caused by disasters in a certain period of history, classifies a corresponding disaster type or disaster influence, determines whether an output result of the first model is a first result, the first result may be a type that the target keyword does not hit the corresponding disaster or a prediction that the target keyword does not have the corresponding disaster influence, when the output result of the first model is the first result, the target keyword is input into a second model, and determines whether an output result of the second model is a second result, the second model may be a market condition (for example, market share, product acceleration curve, sign cost rate) of various insurance policy products of the year by using a clustering algorithm, Pre-estimated endorsement generation efficiency, etc.), the second result may refer to the policy product classification after the target keyword hits the clustering in the second model, and the policy product classification may include: if the output result of the second model is a second result, acquiring parameter information of a plurality of initial objects (policy products) corresponding to the second result, wherein the parameter information comprises: the price, the type, the applicable age interval, the size of the premium and the like of the policy product.
In one embodiment, the second model is obtained by performing a clustering operation on a preset sample object set based on a clustering algorithm, and the specific clustering operation includes:
setting a density radius between each sample object and the number of the minimum sample objects in the density radius, iteratively calculating to obtain a core sample object, a sample object with a reachable density and an edge sample object from all the sample objects based on the density radius and the number of the minimum sample objects, obtaining the sample object with the reachable density of the core sample object, and updating the cluster corresponding to the core sample object by using the sample object with the reachable density obtained by iterative calculation.
The DBSCAN algorithm is a density-based clustering algorithm that generally assumes that classes can be determined by how closely the samples are distributed. The samples of the same category are closely connected, that is, samples of the same category do not exist far around any sample of the category, the closely connected samples are classified into one category, so that a clustering category is obtained, and all groups of closely connected samples are classified into different categories, so that final results of all clustering categories are obtained.
Further, as the selection of the global parameters Eps and MinPts of the DBSCAN algorithm depends on manual intervention, after the data with uniform density distribution are arranged in an ascending order according to the k-dist curve, a point at which the curve variation amplitude begins to rise steeply is manually selected as the Eps parameter, and the MinPts parameter is determined to be a fixed constant 4, the implementation process is complicated, and the manual intervention is depended on.
Therefore, the reasonable global parameters Eps and MinPts can also be determined in an adaptive manner, and part of representative objects are selected as seed objects for class expansion during region query, instead of using the neighborhood objects of all core objects as seeds for class expansion. The process is as follows:
(1) adaptively determining global parameters Eps and MinPts;
(2) classifying all sample points, respectively marking the sample points as core samples, boundary sample points and noise sample points, and deleting marked noise sample points;
(3) connecting all core points with the distances within the Eps distance, and classifying the core points into the same cluster;
(4) selecting a seed representative object corresponding to the core point in each cluster;
(5) and traversing the data sets of the labels of various policy products, performing area query according to the selected representative object, and dividing the boundary points into clusters corresponding to the core points. If all points in the data set are processed, the algorithm ends.
Because the density measurement index is single, the data setMainly for data where the cluster density differences are not significant. Calculating a distance distribution matrix DIST according to the input policy data set D n nxThe formula includes:
where n is the number of objects in the policy dataset D. DIST n nxIs a real symmetric matrix of n rows and n columns, where each element represents the distance between object i and object j (i.e., product policies) in the data set D. Calculating DIST n nxThe value of each element in (a) is then arranged row by row in ascending order. By DIST n ixRepresents DIST n nxThe value of the ith column in, to DIST n ixCarrying out ascending arrangement on each column to obtain KNN distribution;
and carrying out curve fitting on the KNN distribution data obtained by ascending order arrangement by using a polynomial curve fitting formula, wherein the polynomial curve fitting formula is as follows:
wherein, a, b, c, d, e are adjacent 5 sample points, x is the distance between the sample point and the abcde sample point, and x after the quadratic derivation is solved to obtain:
taking the larger value of the x solution, rounding off the smaller value, and substituting it into the above polynomial may result in EPS = f (x) since the smaller value is a point close to the boundary. Determining the MinPts value is that the object number of the Eps-neighborhood of each point is calculated in turn according to the statistical distribution characteristic of the data point of each point field, and then the mathematical expectation of the data object is calculated, wherein the formula is as follows:
where n is the number of objects in the policy dataset D, P i The number of points in the Eps neighborhood at point i.
In one embodiment, the determining module is further configured to:
when the output result of the first model is judged not to be the first result, sending first prompt information to a preset user side; and when the output result of the second model is judged not to be the second result, sending a second prompt message to a preset user side.
When the output result of the first model is judged not to be the first result, the target keyword hits the corresponding disaster type, or the target keyword is predicted to have the corresponding disaster influence, so that the corresponding early warning prompt can be sent to a manager, and when the output result of the second model is judged not to be the second result, the target keyword is not in the existing accumulation of insurance policy products, and the insurance policy can be planned for the insurance policy target or new positive influence is generated on customer service, so that the prompt information can be sent for a product decision maker to refer to.
The pushing module 130 is configured to, based on the parameter information and the preset dimensions of the plurality of initial objects, screen a second preset number of target objects from the plurality of initial objects, and push the target objects to a preset user group.
In this embodiment, a preset number of target objects can be screened from a plurality of initial objects (policy products) according to the continuation policy, the cost policy, the acceleration policy of the plurality of initial objects and the parameter information of the plurality of initial objects, and the target objects are pushed to a preset user group, for example, by calculating and summarizing historical policies, 3 sets of policy products with the highest continuation rate after the customer policy is due under the conditions of different insurance application price intervals, different payment intervals, insurance application time limits, different insurance coverage, different age groups of insurance applicants, different vehicle attributions, and the like are judged;
calculating the insurance policy of the type by the cost dimension, subtracting the payment cost from the application expense under the condition ranges of different sign sheet cost rates, price intervals and the like, obtaining 3 sets of insurance policy products with the optimal difference value within a certain requirement range, calculating 3 sets of insurance policy products with the fastest insurance policy acceleration ratio under different conditions by the acceleration dimension, and pushing the calculated insurance policy products to a preset user group.
In one embodiment, the pushing manner includes a first pushing manner and a second pushing manner, and the first pushing manner includes: when a request for acquiring an object sent by a user in a preset user group is received, carrying out identity authentication on the user, and pushing the target object to the user when the identity authentication passes;
the second pushing manner comprises: and acquiring related information of the preset user group, wherein the related information comprises the IP address of each user, and pushing the target object to the preset user group according to the related information.
In a first push mode (e.g., active acquisition by a user), the user is authenticated upon receiving a request for object acquisition from the user. For example, the device identifier included in the request is obtained, whether the device identifier is a pre-bound device identifier (white list) is judged, if yes, the request is judged to be normal, and if not, the request is judged to be abnormal. And a second pushing mode (for example, passive receiving by the user) acquires the IP address of the user, and pushes the target object to the corresponding user.
Fig. 3 is a schematic diagram of an electronic device 1 according to a preferred embodiment of the invention.
The electronic device 1 includes but is not limited to: memory 11, processor 12, display 13, and network interface 14. The electronic device 1 is connected to a network through a network interface 14 to obtain raw data. The network may be a wireless or wired network such as an Intranet (Intranet), the Internet (Internet), a Global System for Mobile communications (GSM), Wideband Code Division Multiple Access (WCDMA), a 4G network, a 5G network, Bluetooth (Bluetooth), Wi-Fi, or a communication network.
The memory 11 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the storage 11 may be an internal storage unit of the electronic device 1, such as a hard disk or a memory of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like equipped with the electronic device 1. Of course, the memory 11 may also comprise both an internal memory unit and an external memory device of the electronic device 1. In this embodiment, the memory 11 is generally used for storing an operating system and various application software installed in the electronic device 1, such as program codes of the object pushing program 10 based on multi-source data. Further, the memory 11 may also be used to temporarily store various types of data that have been output or are to be output.
Processor 12 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 12 is typically used for controlling the overall operation of the electronic device 1, such as performing data interaction or communication related control and processing. In this embodiment, the processor 12 is configured to run a program code or process data stored in the memory 11, for example, run a program code of the object pushing program 10 based on multi-source data.
The display 13 may be referred to as a display screen or display unit. In some embodiments, the display 13 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an Organic Light-Emitting Diode (OLED) touch screen, or the like. The display 13 is used for displaying information processed in the electronic device 1 and for displaying a visual work interface, e.g. displaying the results of data statistics.
The network interface 14 may optionally comprise a standard wired interface, a wireless interface (e.g. WI-FI interface), the network interface 14 typically being used for establishing a communication connection between the electronic device 1 and other electronic devices.
Fig. 3 only shows the electronic device 1 with components 11-14 and the multi-source data based object push program 10, but it is to be understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead.
Optionally, the electronic device 1 may further comprise a user interface, the user interface may comprise a Display (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface may further comprise a standard wired interface and a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an Organic Light-Emitting Diode (OLED) touch screen, or the like. The display, which may also be referred to as a display screen or display unit, is suitable for displaying information processed in the electronic device 1 and for displaying a visualized user interface, among other things.
The electronic device 1 may further include a Radio Frequency (RF) circuit, a sensor, an audio circuit, and the like, which are not described in detail herein.
In the above embodiment, when the processor 12 executes the multi-source data based object pushing program 10 stored in the memory 11, the following steps may be implemented:
acquiring a specific type of text from a preset database, preprocessing the specific type of text to obtain a plurality of initial keywords, calculating the frequency of the initial keywords appearing in the specific type of text, and screening out a first preset number of target keywords based on the frequency and a preset weight;
inputting the target keywords into a first model, judging whether an output result of the first model is a first result, if so, inputting the target keywords into a second model, judging whether the output result of the second model is a second result, and if so, acquiring parameter information of a plurality of initial objects corresponding to the second result;
and screening a second preset number of target objects from the plurality of initial objects based on the parameter information and preset dimensions of the plurality of initial objects, and pushing the target objects to a preset user group.
The storage device may be the memory 11 of the electronic device 1, or may be another storage device communicatively connected to the electronic device 1.
For detailed description of the above steps, please refer to the above description of fig. 2 regarding a functional block diagram of an embodiment of the object pushing apparatus 100 based on multi-source data and fig. 1 regarding a flowchart of an embodiment of an object pushing method based on multi-source data.
In addition, an embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium may be non-volatile or volatile. The computer readable storage medium may be any one or any combination of hard disks, multimedia cards, SD cards, flash memory cards, SMCs, Read Only Memories (ROMs), Erasable Programmable Read Only Memories (EPROMs), portable compact disc read only memories (CD-ROMs), USB memories, etc. The computer-readable storage medium comprises a storage data area and a storage program area, the storage data area stores data created according to the use of the block chain node, the storage program area stores an object pushing program 10 based on multi-source data, and when being executed by a processor, the object pushing program 10 based on multi-source data realizes the following operations:
acquiring a specific type of text from a preset database, preprocessing the specific type of text to obtain a plurality of initial keywords, calculating the frequency of the initial keywords appearing in the specific type of text, and screening out a first preset number of target keywords based on the frequency and a preset weight;
inputting the target keywords into a first model, judging whether an output result of the first model is a first result, if so, inputting the target keywords into a second model, judging whether the output result of the second model is a second result, and if so, acquiring parameter information of a plurality of initial objects corresponding to the second result;
and screening a second preset number of target objects from the plurality of initial objects based on the parameter information and preset dimensions of the plurality of initial objects, and pushing the target objects to a preset user group.
The specific implementation of the computer-readable storage medium of the present invention is substantially the same as the above-mentioned specific implementation of the object pushing method based on multi-source data, and is not described herein again.
In another embodiment, in order to further ensure the privacy and security of all the appearing data, all the data may be stored in a node of a block chain. Such as target keywords and parameter information, which may be stored in block link points.
It should be noted that the blockchain in the present invention is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
It should be noted that the above-mentioned numbers of the embodiments of the present invention are merely for description, and do not represent the merits of the embodiments. And the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, apparatus, article, or method that includes the element.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention essentially or contributing to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) as described above and includes several instructions for enabling a terminal device (such as a mobile phone, a computer, an electronic device, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A method for pushing an object based on multi-source data is characterized by comprising the following steps:
acquiring a specific type of text from a preset database, preprocessing the specific type of text to obtain a plurality of initial keywords, calculating the frequency of the initial keywords appearing in the specific type of text, and screening out a first preset number of target keywords based on the frequency and a preset weight;
inputting the target keywords into a first model, judging whether an output result of the first model is a first result, if so, inputting the target keywords into a second model, judging whether the output result of the second model is a second result, and if so, acquiring parameter information of a plurality of initial objects corresponding to the second result;
and screening a second preset number of target objects from the plurality of initial objects based on the parameter information and preset dimensions of the plurality of initial objects, and pushing the target objects to a preset user group.
2. The multi-source data-based object pushing method of claim 1, wherein the pre-processing the specific type of text to obtain a plurality of keywords comprises:
and performing word segmentation operation on the specific type of text to obtain a plurality of words, and extracting a plurality of initial keywords from the plurality of words based on a TF-IDF algorithm.
3. The multi-source data-based object pushing method of claim 1 or 2, wherein the selecting a first preset number of target keywords based on the frequency number and a preset weight comprises:
and distributing associated weights for initial keywords with preset parts of speech in the plurality of initial keywords based on a preset distribution rule, calculating scores of the initial keywords based on the frequency and the weights, sequencing the scores from large to small, and selecting a first preset number of initial keywords as the target keywords.
4. The multi-source data-based object pushing method of claim 1, wherein before the obtaining of the specific type of text from the preset database, the method further comprises:
the method comprises the steps of obtaining a text of a specific type from a preset data source in real time, extracting structural information of the text of the specific type, and storing the structural information to a preset database.
5. The multi-source data-based object pushing method of claim 1, wherein the method further comprises:
when the output result of the first model is judged not to be the first result, sending first prompt information to a preset user side;
and when the output result of the second model is judged not to be the second result, sending a second prompt message to a preset user side.
6. The object pushing method based on multi-source data of claim 1, wherein the second model is obtained by performing a clustering operation on a preset sample object set based on a clustering algorithm, and the specific clustering operation comprises:
setting a density radius between each sample object and the number of the minimum sample objects in the density radius, iteratively calculating to obtain a core sample object, a sample object with a reachable density and an edge sample object from all the sample objects based on the density radius and the number of the minimum sample objects, obtaining the sample object with the reachable density of the core sample object, and updating the cluster corresponding to the core sample object by using the sample object with the reachable density obtained by iterative calculation.
7. The multi-source data-based object pushing method of claim 1, wherein the pushing manner comprises a first pushing manner and a second pushing manner, and the first pushing manner comprises: when a request for acquiring an object sent by a user in a preset user group is received, carrying out identity authentication on the user, and pushing the target object to the user when the identity authentication passes;
the second pushing manner comprises: and acquiring related information of the preset user group, wherein the related information comprises the IP address of each user, and pushing the target object to the preset user group according to the related information.
8. An object pushing apparatus based on multi-source data, the apparatus comprising:
a preprocessing module: the system comprises a database, a database and a database server, wherein the database is used for acquiring texts of specific types from a preset database, preprocessing the texts of the specific types to obtain a plurality of initial keywords, calculating the frequency of the initial keywords appearing in the texts of the specific types, and screening out a first preset number of target keywords based on the frequency and a preset weight;
a judging module: the system comprises a first model, a second model and a third model, wherein the first model is used for inputting a target keyword into the first model, judging whether an output result of the first model is a first result, if so, inputting the target keyword into the second model, judging whether an output result of the second model is a second result, and if so, acquiring parameter information of a plurality of initial objects corresponding to the second result;
a pushing module: and the target object selection module is used for screening out a second preset number of target objects from the plurality of initial objects based on the parameter information and preset dimensions of the plurality of initial objects, and pushing the target objects to a preset user group.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a program executable by the at least one processor to enable the at least one processor to perform the multi-source data-based object pushing method of any one of claims 1 to 7.
10. A computer-readable storage medium, wherein the computer-readable storage medium stores a multi-source data-based object pushing program, and when the multi-source data-based object pushing program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.
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